Favorite object extraction using web images

Fanman Meng, Bing Luo, Chao Huang, Liangzhi Tang, Bing Zeng, Nini Rao

Abstract: In this paper, we propose a framework to discover and segment favorite object from the natural images. The main idea is to first generate the shape based common template of the favorite object using the images collected from the web. Then, the common template is used to extract the favorite object from the original images. In the common template generation, co-segmentation is used to provide the initial segments. The median graph theory is employed to construct the common template. We also propose a new shape descriptor namely directional shape representation to handle shape variations. We test our method on the images collected from image datasets and web. Experimental results demonstrate the effectiveness of the proposed method.